|
--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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- mteb |
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model-index: |
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- name: embedder-100p |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 67.05970149253731 |
|
- type: ap |
|
value: 30.376473854922846 |
|
- type: f1 |
|
value: 61.30474831792133 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 70.40857500000001 |
|
- type: ap |
|
value: 64.61611594622543 |
|
- type: f1 |
|
value: 70.28136292034776 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
|
- type: accuracy |
|
value: 33.214 |
|
- type: f1 |
|
value: 33.123322451005755 |
|
- task: |
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type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.311999999999998 |
|
- type: map_at_10 |
|
value: 42.760999999999996 |
|
- type: map_at_100 |
|
value: 43.691 |
|
- type: map_at_1000 |
|
value: 43.698 |
|
- type: map_at_3 |
|
value: 37.091 |
|
- type: map_at_5 |
|
value: 40.398 |
|
- type: mrr_at_1 |
|
value: 28.165000000000003 |
|
- type: mrr_at_10 |
|
value: 43.05 |
|
- type: mrr_at_100 |
|
value: 43.994 |
|
- type: mrr_at_1000 |
|
value: 44.0 |
|
- type: mrr_at_3 |
|
value: 37.376 |
|
- type: mrr_at_5 |
|
value: 40.665 |
|
- type: ndcg_at_1 |
|
value: 27.311999999999998 |
|
- type: ndcg_at_10 |
|
value: 52.035 |
|
- type: ndcg_at_100 |
|
value: 55.891000000000005 |
|
- type: ndcg_at_1000 |
|
value: 56.043 |
|
- type: ndcg_at_3 |
|
value: 40.38 |
|
- type: ndcg_at_5 |
|
value: 46.364 |
|
- type: precision_at_1 |
|
value: 27.311999999999998 |
|
- type: precision_at_10 |
|
value: 8.193 |
|
- type: precision_at_100 |
|
value: 0.985 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 16.643 |
|
- type: precision_at_5 |
|
value: 12.902 |
|
- type: recall_at_1 |
|
value: 27.311999999999998 |
|
- type: recall_at_10 |
|
value: 81.935 |
|
- type: recall_at_100 |
|
value: 98.506 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 49.929 |
|
- type: recall_at_5 |
|
value: 64.509 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 42.899186071418946 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
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split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 32.44851270109027 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 61.05081337796836 |
|
- type: mrr |
|
value: 73.87218045112782 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.06755261269532 |
|
- type: cos_sim_spearman |
|
value: 75.31798123153732 |
|
- type: euclidean_pearson |
|
value: 77.70454789166935 |
|
- type: euclidean_spearman |
|
value: 74.07578425253767 |
|
- type: manhattan_pearson |
|
value: 77.18021593857006 |
|
- type: manhattan_spearman |
|
value: 74.10590542079663 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 82.73051948051948 |
|
- type: f1 |
|
value: 82.61992011434658 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 37.236246179832975 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 29.75182197424716 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: map_at_1 |
|
value: 28.016999999999996 |
|
- type: map_at_10 |
|
value: 39.519999999999996 |
|
- type: map_at_100 |
|
value: 40.987 |
|
- type: map_at_1000 |
|
value: 41.124 |
|
- type: map_at_3 |
|
value: 36.120000000000005 |
|
- type: map_at_5 |
|
value: 38.071 |
|
- type: mrr_at_1 |
|
value: 35.05 |
|
- type: mrr_at_10 |
|
value: 45.589 |
|
- type: mrr_at_100 |
|
value: 46.322 |
|
- type: mrr_at_1000 |
|
value: 46.366 |
|
- type: mrr_at_3 |
|
value: 43.108999999999995 |
|
- type: mrr_at_5 |
|
value: 44.754 |
|
- type: ndcg_at_1 |
|
value: 35.05 |
|
- type: ndcg_at_10 |
|
value: 46.119 |
|
- type: ndcg_at_100 |
|
value: 51.512 |
|
- type: ndcg_at_1000 |
|
value: 53.471000000000004 |
|
- type: ndcg_at_3 |
|
value: 41.3 |
|
- type: ndcg_at_5 |
|
value: 43.657000000000004 |
|
- type: precision_at_1 |
|
value: 35.05 |
|
- type: precision_at_10 |
|
value: 9.156 |
|
- type: precision_at_100 |
|
value: 1.516 |
|
- type: precision_at_1000 |
|
value: 0.201 |
|
- type: precision_at_3 |
|
value: 20.552999999999997 |
|
- type: precision_at_5 |
|
value: 14.793000000000001 |
|
- type: recall_at_1 |
|
value: 28.016999999999996 |
|
- type: recall_at_10 |
|
value: 58.4 |
|
- type: recall_at_100 |
|
value: 81.67699999999999 |
|
- type: recall_at_1000 |
|
value: 94.119 |
|
- type: recall_at_3 |
|
value: 44.293 |
|
- type: recall_at_5 |
|
value: 51.056000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.46 |
|
- type: map_at_10 |
|
value: 33.194 |
|
- type: map_at_100 |
|
value: 34.367999999999995 |
|
- type: map_at_1000 |
|
value: 34.514 |
|
- type: map_at_3 |
|
value: 30.134 |
|
- type: map_at_5 |
|
value: 31.796999999999997 |
|
- type: mrr_at_1 |
|
value: 29.744999999999997 |
|
- type: mrr_at_10 |
|
value: 38.213 |
|
- type: mrr_at_100 |
|
value: 38.942 |
|
- type: mrr_at_1000 |
|
value: 38.993 |
|
- type: mrr_at_3 |
|
value: 35.435 |
|
- type: mrr_at_5 |
|
value: 37.053000000000004 |
|
- type: ndcg_at_1 |
|
value: 29.744999999999997 |
|
- type: ndcg_at_10 |
|
value: 38.868 |
|
- type: ndcg_at_100 |
|
value: 43.562 |
|
- type: ndcg_at_1000 |
|
value: 46.036 |
|
- type: ndcg_at_3 |
|
value: 33.93 |
|
- type: ndcg_at_5 |
|
value: 36.175000000000004 |
|
- type: precision_at_1 |
|
value: 29.744999999999997 |
|
- type: precision_at_10 |
|
value: 7.605 |
|
- type: precision_at_100 |
|
value: 1.291 |
|
- type: precision_at_1000 |
|
value: 0.185 |
|
- type: precision_at_3 |
|
value: 16.582 |
|
- type: precision_at_5 |
|
value: 12.051 |
|
- type: recall_at_1 |
|
value: 23.46 |
|
- type: recall_at_10 |
|
value: 50.080000000000005 |
|
- type: recall_at_100 |
|
value: 70.161 |
|
- type: recall_at_1000 |
|
value: 86.009 |
|
- type: recall_at_3 |
|
value: 36.229 |
|
- type: recall_at_5 |
|
value: 42.055 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 35.515 |
|
- type: map_at_10 |
|
value: 47.028999999999996 |
|
- type: map_at_100 |
|
value: 48.104 |
|
- type: map_at_1000 |
|
value: 48.171 |
|
- type: map_at_3 |
|
value: 44.224000000000004 |
|
- type: map_at_5 |
|
value: 45.795 |
|
- type: mrr_at_1 |
|
value: 40.627 |
|
- type: mrr_at_10 |
|
value: 50.251000000000005 |
|
- type: mrr_at_100 |
|
value: 51.001 |
|
- type: mrr_at_1000 |
|
value: 51.035 |
|
- type: mrr_at_3 |
|
value: 48.046 |
|
- type: mrr_at_5 |
|
value: 49.262 |
|
- type: ndcg_at_1 |
|
value: 40.627 |
|
- type: ndcg_at_10 |
|
value: 52.5 |
|
- type: ndcg_at_100 |
|
value: 56.967999999999996 |
|
- type: ndcg_at_1000 |
|
value: 58.414 |
|
- type: ndcg_at_3 |
|
value: 47.725 |
|
- type: ndcg_at_5 |
|
value: 49.932 |
|
- type: precision_at_1 |
|
value: 40.627 |
|
- type: precision_at_10 |
|
value: 8.464 |
|
- type: precision_at_100 |
|
value: 1.17 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 21.526 |
|
- type: precision_at_5 |
|
value: 14.545 |
|
- type: recall_at_1 |
|
value: 35.515 |
|
- type: recall_at_10 |
|
value: 65.436 |
|
- type: recall_at_100 |
|
value: 85.06 |
|
- type: recall_at_1000 |
|
value: 95.50999999999999 |
|
- type: recall_at_3 |
|
value: 52.339 |
|
- type: recall_at_5 |
|
value: 57.894999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.75 |
|
- type: map_at_10 |
|
value: 27.639999999999997 |
|
- type: map_at_100 |
|
value: 28.612 |
|
- type: map_at_1000 |
|
value: 28.716 |
|
- type: map_at_3 |
|
value: 25.186999999999998 |
|
- type: map_at_5 |
|
value: 26.558999999999997 |
|
- type: mrr_at_1 |
|
value: 21.582 |
|
- type: mrr_at_10 |
|
value: 29.637999999999998 |
|
- type: mrr_at_100 |
|
value: 30.514000000000003 |
|
- type: mrr_at_1000 |
|
value: 30.592999999999996 |
|
- type: mrr_at_3 |
|
value: 27.326 |
|
- type: mrr_at_5 |
|
value: 28.58 |
|
- type: ndcg_at_1 |
|
value: 21.582 |
|
- type: ndcg_at_10 |
|
value: 32.301 |
|
- type: ndcg_at_100 |
|
value: 37.217 |
|
- type: ndcg_at_1000 |
|
value: 39.951 |
|
- type: ndcg_at_3 |
|
value: 27.483999999999998 |
|
- type: ndcg_at_5 |
|
value: 29.754 |
|
- type: precision_at_1 |
|
value: 21.582 |
|
- type: precision_at_10 |
|
value: 5.175 |
|
- type: precision_at_100 |
|
value: 0.803 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 11.940000000000001 |
|
- type: precision_at_5 |
|
value: 8.52 |
|
- type: recall_at_1 |
|
value: 19.75 |
|
- type: recall_at_10 |
|
value: 44.783 |
|
- type: recall_at_100 |
|
value: 67.673 |
|
- type: recall_at_1000 |
|
value: 88.676 |
|
- type: recall_at_3 |
|
value: 31.740000000000002 |
|
- type: recall_at_5 |
|
value: 37.128 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 11.791 |
|
- type: map_at_10 |
|
value: 18.782 |
|
- type: map_at_100 |
|
value: 19.939 |
|
- type: map_at_1000 |
|
value: 20.083000000000002 |
|
- type: map_at_3 |
|
value: 16.564 |
|
- type: map_at_5 |
|
value: 17.592 |
|
- type: mrr_at_1 |
|
value: 15.174000000000001 |
|
- type: mrr_at_10 |
|
value: 22.448999999999998 |
|
- type: mrr_at_100 |
|
value: 23.430999999999997 |
|
- type: mrr_at_1000 |
|
value: 23.521 |
|
- type: mrr_at_3 |
|
value: 20.025000000000002 |
|
- type: mrr_at_5 |
|
value: 21.238 |
|
- type: ndcg_at_1 |
|
value: 15.174000000000001 |
|
- type: ndcg_at_10 |
|
value: 23.411 |
|
- type: ndcg_at_100 |
|
value: 29.365999999999996 |
|
- type: ndcg_at_1000 |
|
value: 32.893 |
|
- type: ndcg_at_3 |
|
value: 18.999 |
|
- type: ndcg_at_5 |
|
value: 20.721 |
|
- type: precision_at_1 |
|
value: 15.174000000000001 |
|
- type: precision_at_10 |
|
value: 4.714 |
|
- type: precision_at_100 |
|
value: 0.903 |
|
- type: precision_at_1000 |
|
value: 0.134 |
|
- type: precision_at_3 |
|
value: 9.494 |
|
- type: precision_at_5 |
|
value: 6.94 |
|
- type: recall_at_1 |
|
value: 11.791 |
|
- type: recall_at_10 |
|
value: 33.986 |
|
- type: recall_at_100 |
|
value: 60.833999999999996 |
|
- type: recall_at_1000 |
|
value: 86.291 |
|
- type: recall_at_3 |
|
value: 21.983 |
|
- type: recall_at_5 |
|
value: 26.313 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.041999999999998 |
|
- type: map_at_10 |
|
value: 35.61 |
|
- type: map_at_100 |
|
value: 37.002 |
|
- type: map_at_1000 |
|
value: 37.120999999999995 |
|
- type: map_at_3 |
|
value: 31.982 |
|
- type: map_at_5 |
|
value: 34.007 |
|
- type: mrr_at_1 |
|
value: 30.895 |
|
- type: mrr_at_10 |
|
value: 41.095 |
|
- type: mrr_at_100 |
|
value: 41.983 |
|
- type: mrr_at_1000 |
|
value: 42.031 |
|
- type: mrr_at_3 |
|
value: 38.114 |
|
- type: mrr_at_5 |
|
value: 39.798 |
|
- type: ndcg_at_1 |
|
value: 30.895 |
|
- type: ndcg_at_10 |
|
value: 42.138999999999996 |
|
- type: ndcg_at_100 |
|
value: 47.741 |
|
- type: ndcg_at_1000 |
|
value: 49.931 |
|
- type: ndcg_at_3 |
|
value: 36.179 |
|
- type: ndcg_at_5 |
|
value: 38.998 |
|
- type: precision_at_1 |
|
value: 30.895 |
|
- type: precision_at_10 |
|
value: 8.065 |
|
- type: precision_at_100 |
|
value: 1.274 |
|
- type: precision_at_1000 |
|
value: 0.165 |
|
- type: precision_at_3 |
|
value: 17.645 |
|
- type: precision_at_5 |
|
value: 12.955 |
|
- type: recall_at_1 |
|
value: 25.041999999999998 |
|
- type: recall_at_10 |
|
value: 56.169999999999995 |
|
- type: recall_at_100 |
|
value: 79.3 |
|
- type: recall_at_1000 |
|
value: 93.618 |
|
- type: recall_at_3 |
|
value: 39.359 |
|
- type: recall_at_5 |
|
value: 46.650000000000006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.854 |
|
- type: map_at_10 |
|
value: 32.088 |
|
- type: map_at_100 |
|
value: 33.511 |
|
- type: map_at_1000 |
|
value: 33.629999999999995 |
|
- type: map_at_3 |
|
value: 29.079 |
|
- type: map_at_5 |
|
value: 30.663 |
|
- type: mrr_at_1 |
|
value: 29.110000000000003 |
|
- type: mrr_at_10 |
|
value: 36.902 |
|
- type: mrr_at_100 |
|
value: 37.927 |
|
- type: mrr_at_1000 |
|
value: 37.99 |
|
- type: mrr_at_3 |
|
value: 34.285 |
|
- type: mrr_at_5 |
|
value: 35.757 |
|
- type: ndcg_at_1 |
|
value: 29.110000000000003 |
|
- type: ndcg_at_10 |
|
value: 37.429 |
|
- type: ndcg_at_100 |
|
value: 43.59 |
|
- type: ndcg_at_1000 |
|
value: 46.207 |
|
- type: ndcg_at_3 |
|
value: 32.394 |
|
- type: ndcg_at_5 |
|
value: 34.562 |
|
- type: precision_at_1 |
|
value: 29.110000000000003 |
|
- type: precision_at_10 |
|
value: 6.895 |
|
- type: precision_at_100 |
|
value: 1.176 |
|
- type: precision_at_1000 |
|
value: 0.158 |
|
- type: precision_at_3 |
|
value: 15.107000000000001 |
|
- type: precision_at_5 |
|
value: 10.982 |
|
- type: recall_at_1 |
|
value: 23.854 |
|
- type: recall_at_10 |
|
value: 48.589 |
|
- type: recall_at_100 |
|
value: 74.78 |
|
- type: recall_at_1000 |
|
value: 92.836 |
|
- type: recall_at_3 |
|
value: 34.489 |
|
- type: recall_at_5 |
|
value: 40.182 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.159999999999997 |
|
- type: map_at_10 |
|
value: 29.421333333333337 |
|
- type: map_at_100 |
|
value: 30.61058333333333 |
|
- type: map_at_1000 |
|
value: 30.742416666666667 |
|
- type: map_at_3 |
|
value: 26.745833333333337 |
|
- type: map_at_5 |
|
value: 28.20291666666667 |
|
- type: mrr_at_1 |
|
value: 25.308249999999997 |
|
- type: mrr_at_10 |
|
value: 33.21275 |
|
- type: mrr_at_100 |
|
value: 34.09341666666666 |
|
- type: mrr_at_1000 |
|
value: 34.163000000000004 |
|
- type: mrr_at_3 |
|
value: 30.81675 |
|
- type: mrr_at_5 |
|
value: 32.16816666666667 |
|
- type: ndcg_at_1 |
|
value: 25.308249999999997 |
|
- type: ndcg_at_10 |
|
value: 34.46208333333333 |
|
- type: ndcg_at_100 |
|
value: 39.77183333333334 |
|
- type: ndcg_at_1000 |
|
value: 42.461916666666674 |
|
- type: ndcg_at_3 |
|
value: 29.797916666666662 |
|
- type: ndcg_at_5 |
|
value: 31.935166666666664 |
|
- type: precision_at_1 |
|
value: 25.308249999999997 |
|
- type: precision_at_10 |
|
value: 6.260916666666666 |
|
- type: precision_at_100 |
|
value: 1.0716666666666665 |
|
- type: precision_at_1000 |
|
value: 0.15025000000000002 |
|
- type: precision_at_3 |
|
value: 13.926916666666667 |
|
- type: precision_at_5 |
|
value: 10.043916666666664 |
|
- type: recall_at_1 |
|
value: 21.159999999999997 |
|
- type: recall_at_10 |
|
value: 45.61408333333334 |
|
- type: recall_at_100 |
|
value: 69.26583333333332 |
|
- type: recall_at_1000 |
|
value: 88.22541666666667 |
|
- type: recall_at_3 |
|
value: 32.67691666666666 |
|
- type: recall_at_5 |
|
value: 38.12716666666667 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.293 |
|
- type: map_at_10 |
|
value: 25.316 |
|
- type: map_at_100 |
|
value: 26.211000000000002 |
|
- type: map_at_1000 |
|
value: 26.316 |
|
- type: map_at_3 |
|
value: 23.200000000000003 |
|
- type: map_at_5 |
|
value: 24.538 |
|
- type: mrr_at_1 |
|
value: 21.471999999999998 |
|
- type: mrr_at_10 |
|
value: 27.583000000000002 |
|
- type: mrr_at_100 |
|
value: 28.371000000000002 |
|
- type: mrr_at_1000 |
|
value: 28.455000000000002 |
|
- type: mrr_at_3 |
|
value: 25.613000000000003 |
|
- type: mrr_at_5 |
|
value: 26.863 |
|
- type: ndcg_at_1 |
|
value: 21.471999999999998 |
|
- type: ndcg_at_10 |
|
value: 28.925 |
|
- type: ndcg_at_100 |
|
value: 33.489000000000004 |
|
- type: ndcg_at_1000 |
|
value: 36.313 |
|
- type: ndcg_at_3 |
|
value: 25.003999999999998 |
|
- type: ndcg_at_5 |
|
value: 27.232 |
|
- type: precision_at_1 |
|
value: 21.471999999999998 |
|
- type: precision_at_10 |
|
value: 4.693 |
|
- type: precision_at_100 |
|
value: 0.762 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 10.838000000000001 |
|
- type: precision_at_5 |
|
value: 7.945 |
|
- type: recall_at_1 |
|
value: 19.293 |
|
- type: recall_at_10 |
|
value: 37.63 |
|
- type: recall_at_100 |
|
value: 58.818000000000005 |
|
- type: recall_at_1000 |
|
value: 80.026 |
|
- type: recall_at_3 |
|
value: 27.389000000000003 |
|
- type: recall_at_5 |
|
value: 32.71 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.087 |
|
- type: map_at_10 |
|
value: 17.777 |
|
- type: map_at_100 |
|
value: 18.837 |
|
- type: map_at_1000 |
|
value: 18.973000000000003 |
|
- type: map_at_3 |
|
value: 15.956999999999999 |
|
- type: map_at_5 |
|
value: 16.902 |
|
- type: mrr_at_1 |
|
value: 14.763000000000002 |
|
- type: mrr_at_10 |
|
value: 20.8 |
|
- type: mrr_at_100 |
|
value: 21.757 |
|
- type: mrr_at_1000 |
|
value: 21.85 |
|
- type: mrr_at_3 |
|
value: 18.989 |
|
- type: mrr_at_5 |
|
value: 19.905 |
|
- type: ndcg_at_1 |
|
value: 14.763000000000002 |
|
- type: ndcg_at_10 |
|
value: 21.512999999999998 |
|
- type: ndcg_at_100 |
|
value: 26.822000000000003 |
|
- type: ndcg_at_1000 |
|
value: 30.270999999999997 |
|
- type: ndcg_at_3 |
|
value: 18.16 |
|
- type: ndcg_at_5 |
|
value: 19.573999999999998 |
|
- type: precision_at_1 |
|
value: 14.763000000000002 |
|
- type: precision_at_10 |
|
value: 4.043 |
|
- type: precision_at_100 |
|
value: 0.7979999999999999 |
|
- type: precision_at_1000 |
|
value: 0.128 |
|
- type: precision_at_3 |
|
value: 8.741 |
|
- type: precision_at_5 |
|
value: 6.325 |
|
- type: recall_at_1 |
|
value: 12.087 |
|
- type: recall_at_10 |
|
value: 29.805 |
|
- type: recall_at_100 |
|
value: 53.787 |
|
- type: recall_at_1000 |
|
value: 78.884 |
|
- type: recall_at_3 |
|
value: 20.497 |
|
- type: recall_at_5 |
|
value: 24.148 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.099 |
|
- type: map_at_10 |
|
value: 29.487999999999996 |
|
- type: map_at_100 |
|
value: 30.553 |
|
- type: map_at_1000 |
|
value: 30.669999999999998 |
|
- type: map_at_3 |
|
value: 27.250000000000004 |
|
- type: map_at_5 |
|
value: 28.416000000000004 |
|
- type: mrr_at_1 |
|
value: 26.026 |
|
- type: mrr_at_10 |
|
value: 33.238 |
|
- type: mrr_at_100 |
|
value: 34.114 |
|
- type: mrr_at_1000 |
|
value: 34.188 |
|
- type: mrr_at_3 |
|
value: 31.157 |
|
- type: mrr_at_5 |
|
value: 32.262 |
|
- type: ndcg_at_1 |
|
value: 26.026 |
|
- type: ndcg_at_10 |
|
value: 34.036 |
|
- type: ndcg_at_100 |
|
value: 39.443 |
|
- type: ndcg_at_1000 |
|
value: 42.181999999999995 |
|
- type: ndcg_at_3 |
|
value: 29.942 |
|
- type: ndcg_at_5 |
|
value: 31.682 |
|
- type: precision_at_1 |
|
value: 26.026 |
|
- type: precision_at_10 |
|
value: 5.7090000000000005 |
|
- type: precision_at_100 |
|
value: 0.9560000000000001 |
|
- type: precision_at_1000 |
|
value: 0.131 |
|
- type: precision_at_3 |
|
value: 13.495 |
|
- type: precision_at_5 |
|
value: 9.366 |
|
- type: recall_at_1 |
|
value: 22.099 |
|
- type: recall_at_10 |
|
value: 44.098 |
|
- type: recall_at_100 |
|
value: 68.726 |
|
- type: recall_at_1000 |
|
value: 87.992 |
|
- type: recall_at_3 |
|
value: 32.902 |
|
- type: recall_at_5 |
|
value: 37.389 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.195 |
|
- type: map_at_10 |
|
value: 27.298000000000002 |
|
- type: map_at_100 |
|
value: 28.875 |
|
- type: map_at_1000 |
|
value: 29.152 |
|
- type: map_at_3 |
|
value: 24.595 |
|
- type: map_at_5 |
|
value: 25.926 |
|
- type: mrr_at_1 |
|
value: 23.913 |
|
- type: mrr_at_10 |
|
value: 31.696999999999996 |
|
- type: mrr_at_100 |
|
value: 32.728 |
|
- type: mrr_at_1000 |
|
value: 32.808 |
|
- type: mrr_at_3 |
|
value: 29.249000000000002 |
|
- type: mrr_at_5 |
|
value: 30.623 |
|
- type: ndcg_at_1 |
|
value: 23.913 |
|
- type: ndcg_at_10 |
|
value: 32.745999999999995 |
|
- type: ndcg_at_100 |
|
value: 38.663 |
|
- type: ndcg_at_1000 |
|
value: 41.984 |
|
- type: ndcg_at_3 |
|
value: 28.272000000000002 |
|
- type: ndcg_at_5 |
|
value: 30.184 |
|
- type: precision_at_1 |
|
value: 23.913 |
|
- type: precision_at_10 |
|
value: 6.601 |
|
- type: precision_at_100 |
|
value: 1.462 |
|
- type: precision_at_1000 |
|
value: 0.241 |
|
- type: precision_at_3 |
|
value: 13.439 |
|
- type: precision_at_5 |
|
value: 10.079 |
|
- type: recall_at_1 |
|
value: 19.195 |
|
- type: recall_at_10 |
|
value: 42.933 |
|
- type: recall_at_100 |
|
value: 69.762 |
|
- type: recall_at_1000 |
|
value: 91.57 |
|
- type: recall_at_3 |
|
value: 30.302 |
|
- type: recall_at_5 |
|
value: 35.17 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.816999999999998 |
|
- type: map_at_10 |
|
value: 19.314 |
|
- type: map_at_100 |
|
value: 20.328 |
|
- type: map_at_1000 |
|
value: 20.439 |
|
- type: map_at_3 |
|
value: 16.658 |
|
- type: map_at_5 |
|
value: 18.169 |
|
- type: mrr_at_1 |
|
value: 15.342 |
|
- type: mrr_at_10 |
|
value: 21.098 |
|
- type: mrr_at_100 |
|
value: 22.031 |
|
- type: mrr_at_1000 |
|
value: 22.126 |
|
- type: mrr_at_3 |
|
value: 18.453 |
|
- type: mrr_at_5 |
|
value: 19.923 |
|
- type: ndcg_at_1 |
|
value: 15.342 |
|
- type: ndcg_at_10 |
|
value: 23.558 |
|
- type: ndcg_at_100 |
|
value: 28.889 |
|
- type: ndcg_at_1000 |
|
value: 31.89 |
|
- type: ndcg_at_3 |
|
value: 18.186 |
|
- type: ndcg_at_5 |
|
value: 20.751 |
|
- type: precision_at_1 |
|
value: 15.342 |
|
- type: precision_at_10 |
|
value: 4.011 |
|
- type: precision_at_100 |
|
value: 0.749 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_3 |
|
value: 7.763000000000001 |
|
- type: precision_at_5 |
|
value: 6.026 |
|
- type: recall_at_1 |
|
value: 13.816999999999998 |
|
- type: recall_at_10 |
|
value: 35.459 |
|
- type: recall_at_100 |
|
value: 60.612 |
|
- type: recall_at_1000 |
|
value: 83.174 |
|
- type: recall_at_3 |
|
value: 20.601 |
|
- type: recall_at_5 |
|
value: 26.83 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.770999999999999 |
|
- type: map_at_10 |
|
value: 14.948 |
|
- type: map_at_100 |
|
value: 16.668 |
|
- type: map_at_1000 |
|
value: 16.865 |
|
- type: map_at_3 |
|
value: 12.264 |
|
- type: map_at_5 |
|
value: 13.623 |
|
- type: mrr_at_1 |
|
value: 18.502 |
|
- type: mrr_at_10 |
|
value: 28.782000000000004 |
|
- type: mrr_at_100 |
|
value: 29.875 |
|
- type: mrr_at_1000 |
|
value: 29.929 |
|
- type: mrr_at_3 |
|
value: 25.147000000000002 |
|
- type: mrr_at_5 |
|
value: 27.322000000000003 |
|
- type: ndcg_at_1 |
|
value: 18.502 |
|
- type: ndcg_at_10 |
|
value: 21.815 |
|
- type: ndcg_at_100 |
|
value: 29.174 |
|
- type: ndcg_at_1000 |
|
value: 32.946999999999996 |
|
- type: ndcg_at_3 |
|
value: 16.833000000000002 |
|
- type: ndcg_at_5 |
|
value: 18.792 |
|
- type: precision_at_1 |
|
value: 18.502 |
|
- type: precision_at_10 |
|
value: 7.016 |
|
- type: precision_at_100 |
|
value: 1.486 |
|
- type: precision_at_1000 |
|
value: 0.219 |
|
- type: precision_at_3 |
|
value: 12.421 |
|
- type: precision_at_5 |
|
value: 10.15 |
|
- type: recall_at_1 |
|
value: 8.770999999999999 |
|
- type: recall_at_10 |
|
value: 27.542 |
|
- type: recall_at_100 |
|
value: 53.481 |
|
- type: recall_at_1000 |
|
value: 74.67399999999999 |
|
- type: recall_at_3 |
|
value: 15.986 |
|
- type: recall_at_5 |
|
value: 20.669 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.0249999999999995 |
|
- type: map_at_10 |
|
value: 11.924 |
|
- type: map_at_100 |
|
value: 15.801000000000002 |
|
- type: map_at_1000 |
|
value: 16.878999999999998 |
|
- type: map_at_3 |
|
value: 9.031 |
|
- type: map_at_5 |
|
value: 10.181 |
|
- type: mrr_at_1 |
|
value: 48.0 |
|
- type: mrr_at_10 |
|
value: 56.928 |
|
- type: mrr_at_100 |
|
value: 57.619 |
|
- type: mrr_at_1000 |
|
value: 57.646 |
|
- type: mrr_at_3 |
|
value: 55.25 |
|
- type: mrr_at_5 |
|
value: 55.974999999999994 |
|
- type: ndcg_at_1 |
|
value: 36.875 |
|
- type: ndcg_at_10 |
|
value: 26.508 |
|
- type: ndcg_at_100 |
|
value: 29.692 |
|
- type: ndcg_at_1000 |
|
value: 36.658 |
|
- type: ndcg_at_3 |
|
value: 30.764000000000003 |
|
- type: ndcg_at_5 |
|
value: 28.049000000000003 |
|
- type: precision_at_1 |
|
value: 48.0 |
|
- type: precision_at_10 |
|
value: 21.175 |
|
- type: precision_at_100 |
|
value: 6.535 |
|
- type: precision_at_1000 |
|
value: 1.6230000000000002 |
|
- type: precision_at_3 |
|
value: 34.75 |
|
- type: precision_at_5 |
|
value: 27.700000000000003 |
|
- type: recall_at_1 |
|
value: 6.0249999999999995 |
|
- type: recall_at_10 |
|
value: 16.454 |
|
- type: recall_at_100 |
|
value: 35.026 |
|
- type: recall_at_1000 |
|
value: 58.031 |
|
- type: recall_at_3 |
|
value: 10.058 |
|
- type: recall_at_5 |
|
value: 12.145999999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 43.470000000000006 |
|
- type: f1 |
|
value: 39.27142511079909 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.468 |
|
- type: map_at_10 |
|
value: 49.652 |
|
- type: map_at_100 |
|
value: 50.314 |
|
- type: map_at_1000 |
|
value: 50.346999999999994 |
|
- type: map_at_3 |
|
value: 46.592 |
|
- type: map_at_5 |
|
value: 48.553000000000004 |
|
- type: mrr_at_1 |
|
value: 40.384 |
|
- type: mrr_at_10 |
|
value: 53.03099999999999 |
|
- type: mrr_at_100 |
|
value: 53.629000000000005 |
|
- type: mrr_at_1000 |
|
value: 53.65299999999999 |
|
- type: mrr_at_3 |
|
value: 49.967 |
|
- type: mrr_at_5 |
|
value: 51.951 |
|
- type: ndcg_at_1 |
|
value: 40.384 |
|
- type: ndcg_at_10 |
|
value: 56.318 |
|
- type: ndcg_at_100 |
|
value: 59.43000000000001 |
|
- type: ndcg_at_1000 |
|
value: 60.266 |
|
- type: ndcg_at_3 |
|
value: 50.341 |
|
- type: ndcg_at_5 |
|
value: 53.756 |
|
- type: precision_at_1 |
|
value: 40.384 |
|
- type: precision_at_10 |
|
value: 8.062999999999999 |
|
- type: precision_at_100 |
|
value: 0.972 |
|
- type: precision_at_1000 |
|
value: 0.106 |
|
- type: precision_at_3 |
|
value: 20.897 |
|
- type: precision_at_5 |
|
value: 14.374 |
|
- type: recall_at_1 |
|
value: 37.468 |
|
- type: recall_at_10 |
|
value: 73.68900000000001 |
|
- type: recall_at_100 |
|
value: 87.844 |
|
- type: recall_at_1000 |
|
value: 94.098 |
|
- type: recall_at_3 |
|
value: 57.768 |
|
- type: recall_at_5 |
|
value: 65.979 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.071 |
|
- type: map_at_10 |
|
value: 23.455000000000002 |
|
- type: map_at_100 |
|
value: 25.358999999999998 |
|
- type: map_at_1000 |
|
value: 25.55 |
|
- type: map_at_3 |
|
value: 20.164 |
|
- type: map_at_5 |
|
value: 21.654999999999998 |
|
- type: mrr_at_1 |
|
value: 28.395 |
|
- type: mrr_at_10 |
|
value: 37.21 |
|
- type: mrr_at_100 |
|
value: 38.086999999999996 |
|
- type: mrr_at_1000 |
|
value: 38.145 |
|
- type: mrr_at_3 |
|
value: 34.336 |
|
- type: mrr_at_5 |
|
value: 35.795 |
|
- type: ndcg_at_1 |
|
value: 28.395 |
|
- type: ndcg_at_10 |
|
value: 30.595 |
|
- type: ndcg_at_100 |
|
value: 37.885000000000005 |
|
- type: ndcg_at_1000 |
|
value: 41.55 |
|
- type: ndcg_at_3 |
|
value: 26.858999999999998 |
|
- type: ndcg_at_5 |
|
value: 27.528999999999996 |
|
- type: precision_at_1 |
|
value: 28.395 |
|
- type: precision_at_10 |
|
value: 8.92 |
|
- type: precision_at_100 |
|
value: 1.6389999999999998 |
|
- type: precision_at_1000 |
|
value: 0.22999999999999998 |
|
- type: precision_at_3 |
|
value: 18.004 |
|
- type: precision_at_5 |
|
value: 13.302 |
|
- type: recall_at_1 |
|
value: 14.071 |
|
- type: recall_at_10 |
|
value: 37.635000000000005 |
|
- type: recall_at_100 |
|
value: 65.18599999999999 |
|
- type: recall_at_1000 |
|
value: 87.58399999999999 |
|
- type: recall_at_3 |
|
value: 24.490000000000002 |
|
- type: recall_at_5 |
|
value: 28.621999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.659 |
|
- type: map_at_10 |
|
value: 33.622 |
|
- type: map_at_100 |
|
value: 34.488 |
|
- type: map_at_1000 |
|
value: 34.58 |
|
- type: map_at_3 |
|
value: 31.317 |
|
- type: map_at_5 |
|
value: 32.689 |
|
- type: mrr_at_1 |
|
value: 49.318 |
|
- type: mrr_at_10 |
|
value: 57.028999999999996 |
|
- type: mrr_at_100 |
|
value: 57.567 |
|
- type: mrr_at_1000 |
|
value: 57.603 |
|
- type: mrr_at_3 |
|
value: 55.152 |
|
- type: mrr_at_5 |
|
value: 56.289 |
|
- type: ndcg_at_1 |
|
value: 49.318 |
|
- type: ndcg_at_10 |
|
value: 42.091 |
|
- type: ndcg_at_100 |
|
value: 45.812999999999995 |
|
- type: ndcg_at_1000 |
|
value: 47.902 |
|
- type: ndcg_at_3 |
|
value: 38.012 |
|
- type: ndcg_at_5 |
|
value: 40.160000000000004 |
|
- type: precision_at_1 |
|
value: 49.318 |
|
- type: precision_at_10 |
|
value: 8.921 |
|
- type: precision_at_100 |
|
value: 1.189 |
|
- type: precision_at_1000 |
|
value: 0.147 |
|
- type: precision_at_3 |
|
value: 23.655 |
|
- type: precision_at_5 |
|
value: 15.897 |
|
- type: recall_at_1 |
|
value: 24.659 |
|
- type: recall_at_10 |
|
value: 44.605 |
|
- type: recall_at_100 |
|
value: 59.453 |
|
- type: recall_at_1000 |
|
value: 73.40299999999999 |
|
- type: recall_at_3 |
|
value: 35.483 |
|
- type: recall_at_5 |
|
value: 39.743 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 67.2992 |
|
- type: ap |
|
value: 61.82215741645874 |
|
- type: f1 |
|
value: 67.04790333380426 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.635 |
|
- type: map_at_10 |
|
value: 22.412000000000003 |
|
- type: map_at_100 |
|
value: 23.622 |
|
- type: map_at_1000 |
|
value: 23.707 |
|
- type: map_at_3 |
|
value: 19.368 |
|
- type: map_at_5 |
|
value: 21.095 |
|
- type: mrr_at_1 |
|
value: 14.04 |
|
- type: mrr_at_10 |
|
value: 22.858 |
|
- type: mrr_at_100 |
|
value: 24.049 |
|
- type: mrr_at_1000 |
|
value: 24.127000000000002 |
|
- type: mrr_at_3 |
|
value: 19.852 |
|
- type: mrr_at_5 |
|
value: 21.552 |
|
- type: ndcg_at_1 |
|
value: 14.04 |
|
- type: ndcg_at_10 |
|
value: 27.676000000000002 |
|
- type: ndcg_at_100 |
|
value: 33.917 |
|
- type: ndcg_at_1000 |
|
value: 36.217 |
|
- type: ndcg_at_3 |
|
value: 21.432000000000002 |
|
- type: ndcg_at_5 |
|
value: 24.519 |
|
- type: precision_at_1 |
|
value: 14.04 |
|
- type: precision_at_10 |
|
value: 4.585999999999999 |
|
- type: precision_at_100 |
|
value: 0.776 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 9.298 |
|
- type: precision_at_5 |
|
value: 7.135 |
|
- type: recall_at_1 |
|
value: 13.635 |
|
- type: recall_at_10 |
|
value: 44.015 |
|
- type: recall_at_100 |
|
value: 73.756 |
|
- type: recall_at_1000 |
|
value: 91.743 |
|
- type: recall_at_3 |
|
value: 26.941 |
|
- type: recall_at_5 |
|
value: 34.378 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 91.81714546283631 |
|
- type: f1 |
|
value: 91.67516531750526 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 74.69904240766073 |
|
- type: f1 |
|
value: 57.9559746458099 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 71.76866173503699 |
|
- type: f1 |
|
value: 69.95643410077002 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.85137861466038 |
|
- type: f1 |
|
value: 77.66496420028315 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 36.646200212660744 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 32.57381797665868 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.54815546178676 |
|
- type: mrr |
|
value: 31.40311212966208 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.005 |
|
- type: map_at_10 |
|
value: 8.125 |
|
- type: map_at_100 |
|
value: 11.439 |
|
- type: map_at_1000 |
|
value: 12.908 |
|
- type: map_at_3 |
|
value: 5.299 |
|
- type: map_at_5 |
|
value: 6.654 |
|
- type: mrr_at_1 |
|
value: 33.745999999999995 |
|
- type: mrr_at_10 |
|
value: 43.513000000000005 |
|
- type: mrr_at_100 |
|
value: 44.330999999999996 |
|
- type: mrr_at_1000 |
|
value: 44.388 |
|
- type: mrr_at_3 |
|
value: 41.28 |
|
- type: mrr_at_5 |
|
value: 42.766 |
|
- type: ndcg_at_1 |
|
value: 31.889 |
|
- type: ndcg_at_10 |
|
value: 26.432 |
|
- type: ndcg_at_100 |
|
value: 26.191 |
|
- type: ndcg_at_1000 |
|
value: 35.413 |
|
- type: ndcg_at_3 |
|
value: 29.625 |
|
- type: ndcg_at_5 |
|
value: 28.588 |
|
- type: precision_at_1 |
|
value: 33.745999999999995 |
|
- type: precision_at_10 |
|
value: 21.146 |
|
- type: precision_at_100 |
|
value: 7.736999999999999 |
|
- type: precision_at_1000 |
|
value: 2.08 |
|
- type: precision_at_3 |
|
value: 29.102 |
|
- type: precision_at_5 |
|
value: 26.316 |
|
- type: recall_at_1 |
|
value: 3.005 |
|
- type: recall_at_10 |
|
value: 12.29 |
|
- type: recall_at_100 |
|
value: 30.06 |
|
- type: recall_at_1000 |
|
value: 63.148 |
|
- type: recall_at_3 |
|
value: 6.587 |
|
- type: recall_at_5 |
|
value: 9.095 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.839000000000002 |
|
- type: map_at_10 |
|
value: 31.424999999999997 |
|
- type: map_at_100 |
|
value: 32.641999999999996 |
|
- type: map_at_1000 |
|
value: 32.704 |
|
- type: map_at_3 |
|
value: 27.742 |
|
- type: map_at_5 |
|
value: 29.854999999999997 |
|
- type: mrr_at_1 |
|
value: 22.451 |
|
- type: mrr_at_10 |
|
value: 33.632 |
|
- type: mrr_at_100 |
|
value: 34.653 |
|
- type: mrr_at_1000 |
|
value: 34.699000000000005 |
|
- type: mrr_at_3 |
|
value: 30.427 |
|
- type: mrr_at_5 |
|
value: 32.263 |
|
- type: ndcg_at_1 |
|
value: 22.422 |
|
- type: ndcg_at_10 |
|
value: 37.929 |
|
- type: ndcg_at_100 |
|
value: 43.667 |
|
- type: ndcg_at_1000 |
|
value: 45.231 |
|
- type: ndcg_at_3 |
|
value: 30.814999999999998 |
|
- type: ndcg_at_5 |
|
value: 34.379 |
|
- type: precision_at_1 |
|
value: 22.422 |
|
- type: precision_at_10 |
|
value: 6.59 |
|
- type: precision_at_100 |
|
value: 0.9860000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11399999999999999 |
|
- type: precision_at_3 |
|
value: 14.301 |
|
- type: precision_at_5 |
|
value: 10.626 |
|
- type: recall_at_1 |
|
value: 19.839000000000002 |
|
- type: recall_at_10 |
|
value: 55.769999999999996 |
|
- type: recall_at_100 |
|
value: 81.733 |
|
- type: recall_at_1000 |
|
value: 93.559 |
|
- type: recall_at_3 |
|
value: 37.078 |
|
- type: recall_at_5 |
|
value: 45.318999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 67.534 |
|
- type: map_at_10 |
|
value: 81.449 |
|
- type: map_at_100 |
|
value: 82.15400000000001 |
|
- type: map_at_1000 |
|
value: 82.173 |
|
- type: map_at_3 |
|
value: 78.412 |
|
- type: map_at_5 |
|
value: 80.268 |
|
- type: mrr_at_1 |
|
value: 77.77 |
|
- type: mrr_at_10 |
|
value: 84.60499999999999 |
|
- type: mrr_at_100 |
|
value: 84.765 |
|
- type: mrr_at_1000 |
|
value: 84.76700000000001 |
|
- type: mrr_at_3 |
|
value: 83.493 |
|
- type: mrr_at_5 |
|
value: 84.221 |
|
- type: ndcg_at_1 |
|
value: 77.79 |
|
- type: ndcg_at_10 |
|
value: 85.555 |
|
- type: ndcg_at_100 |
|
value: 87.105 |
|
- type: ndcg_at_1000 |
|
value: 87.261 |
|
- type: ndcg_at_3 |
|
value: 82.401 |
|
- type: ndcg_at_5 |
|
value: 84.071 |
|
- type: precision_at_1 |
|
value: 77.79 |
|
- type: precision_at_10 |
|
value: 13.104 |
|
- type: precision_at_100 |
|
value: 1.5190000000000001 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 36.157000000000004 |
|
- type: precision_at_5 |
|
value: 23.86 |
|
- type: recall_at_1 |
|
value: 67.534 |
|
- type: recall_at_10 |
|
value: 93.573 |
|
- type: recall_at_100 |
|
value: 99.10799999999999 |
|
- type: recall_at_1000 |
|
value: 99.911 |
|
- type: recall_at_3 |
|
value: 84.575 |
|
- type: recall_at_5 |
|
value: 89.251 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 50.622402916164575 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 54.43689895218044 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.723 |
|
- type: map_at_10 |
|
value: 9.524000000000001 |
|
- type: map_at_100 |
|
value: 11.407 |
|
- type: map_at_1000 |
|
value: 11.721 |
|
- type: map_at_3 |
|
value: 6.678000000000001 |
|
- type: map_at_5 |
|
value: 7.881 |
|
- type: mrr_at_1 |
|
value: 18.2 |
|
- type: mrr_at_10 |
|
value: 28.349999999999998 |
|
- type: mrr_at_100 |
|
value: 29.528 |
|
- type: mrr_at_1000 |
|
value: 29.601 |
|
- type: mrr_at_3 |
|
value: 25.15 |
|
- type: mrr_at_5 |
|
value: 26.765 |
|
- type: ndcg_at_1 |
|
value: 18.2 |
|
- type: ndcg_at_10 |
|
value: 16.603 |
|
- type: ndcg_at_100 |
|
value: 24.331 |
|
- type: ndcg_at_1000 |
|
value: 30.086000000000002 |
|
- type: ndcg_at_3 |
|
value: 15.151 |
|
- type: ndcg_at_5 |
|
value: 13.199 |
|
- type: precision_at_1 |
|
value: 18.2 |
|
- type: precision_at_10 |
|
value: 8.86 |
|
- type: precision_at_100 |
|
value: 2.012 |
|
- type: precision_at_1000 |
|
value: 0.33999999999999997 |
|
- type: precision_at_3 |
|
value: 14.2 |
|
- type: precision_at_5 |
|
value: 11.559999999999999 |
|
- type: recall_at_1 |
|
value: 3.723 |
|
- type: recall_at_10 |
|
value: 17.965 |
|
- type: recall_at_100 |
|
value: 40.803 |
|
- type: recall_at_1000 |
|
value: 69.053 |
|
- type: recall_at_3 |
|
value: 8.633000000000001 |
|
- type: recall_at_5 |
|
value: 11.722000000000001 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.92797679109452 |
|
- type: cos_sim_spearman |
|
value: 80.91205372065706 |
|
- type: euclidean_pearson |
|
value: 83.1339233055303 |
|
- type: euclidean_spearman |
|
value: 80.80406858672507 |
|
- type: manhattan_pearson |
|
value: 83.023350668501 |
|
- type: manhattan_spearman |
|
value: 80.79924041758802 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.40179876416202 |
|
- type: cos_sim_spearman |
|
value: 76.97735281189986 |
|
- type: euclidean_pearson |
|
value: 81.78242131839902 |
|
- type: euclidean_spearman |
|
value: 75.2853626575815 |
|
- type: manhattan_pearson |
|
value: 81.38214640501 |
|
- type: manhattan_spearman |
|
value: 74.96725680962342 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.38943723638555 |
|
- type: cos_sim_spearman |
|
value: 82.62953855483207 |
|
- type: euclidean_pearson |
|
value: 82.4417464172415 |
|
- type: euclidean_spearman |
|
value: 82.8241086805702 |
|
- type: manhattan_pearson |
|
value: 82.05925934320744 |
|
- type: manhattan_spearman |
|
value: 82.44019953304266 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.56920959786761 |
|
- type: cos_sim_spearman |
|
value: 77.83933203825715 |
|
- type: euclidean_pearson |
|
value: 81.34174603327101 |
|
- type: euclidean_spearman |
|
value: 78.05064087128034 |
|
- type: manhattan_pearson |
|
value: 81.1754246859513 |
|
- type: manhattan_spearman |
|
value: 77.8965324094323 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.70673290528633 |
|
- type: cos_sim_spearman |
|
value: 85.918072169933 |
|
- type: euclidean_pearson |
|
value: 85.49668339564212 |
|
- type: euclidean_spearman |
|
value: 86.07562791847965 |
|
- type: manhattan_pearson |
|
value: 85.46112200749786 |
|
- type: manhattan_spearman |
|
value: 86.06360174588102 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.57362584144626 |
|
- type: cos_sim_spearman |
|
value: 80.68461073524229 |
|
- type: euclidean_pearson |
|
value: 81.86974700030184 |
|
- type: euclidean_spearman |
|
value: 81.9556672243023 |
|
- type: manhattan_pearson |
|
value: 81.58501319903948 |
|
- type: manhattan_spearman |
|
value: 81.65934304491222 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 89.0517739143147 |
|
- type: cos_sim_spearman |
|
value: 88.99264497015508 |
|
- type: euclidean_pearson |
|
value: 88.60143851830212 |
|
- type: euclidean_spearman |
|
value: 88.417049574577 |
|
- type: manhattan_pearson |
|
value: 88.71275731832226 |
|
- type: manhattan_spearman |
|
value: 88.62174073802386 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 65.92377536840165 |
|
- type: cos_sim_spearman |
|
value: 68.25861908141049 |
|
- type: euclidean_pearson |
|
value: 67.74046365058068 |
|
- type: euclidean_spearman |
|
value: 67.74440638624723 |
|
- type: manhattan_pearson |
|
value: 67.72314553247108 |
|
- type: manhattan_spearman |
|
value: 67.58993746063668 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.01280212650944 |
|
- type: cos_sim_spearman |
|
value: 84.2021805427655 |
|
- type: euclidean_pearson |
|
value: 85.2593711183253 |
|
- type: euclidean_spearman |
|
value: 84.7692260813728 |
|
- type: manhattan_pearson |
|
value: 85.20370142077513 |
|
- type: manhattan_spearman |
|
value: 84.68261435873887 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 79.8274674627466 |
|
- type: mrr |
|
value: 93.2766625168586 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 44.917 |
|
- type: map_at_10 |
|
value: 54.809 |
|
- type: map_at_100 |
|
value: 55.544000000000004 |
|
- type: map_at_1000 |
|
value: 55.584999999999994 |
|
- type: map_at_3 |
|
value: 51.274 |
|
- type: map_at_5 |
|
value: 53.42 |
|
- type: mrr_at_1 |
|
value: 47.0 |
|
- type: mrr_at_10 |
|
value: 56.00000000000001 |
|
- type: mrr_at_100 |
|
value: 56.611 |
|
- type: mrr_at_1000 |
|
value: 56.647000000000006 |
|
- type: mrr_at_3 |
|
value: 53.166999999999994 |
|
- type: mrr_at_5 |
|
value: 54.883 |
|
- type: ndcg_at_1 |
|
value: 47.0 |
|
- type: ndcg_at_10 |
|
value: 59.948 |
|
- type: ndcg_at_100 |
|
value: 63.214999999999996 |
|
- type: ndcg_at_1000 |
|
value: 64.331 |
|
- type: ndcg_at_3 |
|
value: 53.690000000000005 |
|
- type: ndcg_at_5 |
|
value: 56.99999999999999 |
|
- type: precision_at_1 |
|
value: 47.0 |
|
- type: precision_at_10 |
|
value: 8.433 |
|
- type: precision_at_100 |
|
value: 1.0170000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 21.0 |
|
- type: precision_at_5 |
|
value: 14.667 |
|
- type: recall_at_1 |
|
value: 44.917 |
|
- type: recall_at_10 |
|
value: 74.483 |
|
- type: recall_at_100 |
|
value: 89.1 |
|
- type: recall_at_1000 |
|
value: 98.0 |
|
- type: recall_at_3 |
|
value: 58.15 |
|
- type: recall_at_5 |
|
value: 66.033 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.66534653465347 |
|
- type: cos_sim_ap |
|
value: 90.67883265196161 |
|
- type: cos_sim_f1 |
|
value: 82.81327389796928 |
|
- type: cos_sim_precision |
|
value: 82.04121687929342 |
|
- type: cos_sim_recall |
|
value: 83.6 |
|
- type: dot_accuracy |
|
value: 99.6009900990099 |
|
- type: dot_ap |
|
value: 85.37859415933599 |
|
- type: dot_f1 |
|
value: 79.68285431119922 |
|
- type: dot_precision |
|
value: 78.97838899803537 |
|
- type: dot_recall |
|
value: 80.4 |
|
- type: euclidean_accuracy |
|
value: 99.66435643564357 |
|
- type: euclidean_ap |
|
value: 90.28983244955695 |
|
- type: euclidean_f1 |
|
value: 82.47925817471938 |
|
- type: euclidean_precision |
|
value: 80.55290753098188 |
|
- type: euclidean_recall |
|
value: 84.5 |
|
- type: manhattan_accuracy |
|
value: 99.65247524752475 |
|
- type: manhattan_ap |
|
value: 89.75455076116366 |
|
- type: manhattan_f1 |
|
value: 81.63682864450128 |
|
- type: manhattan_precision |
|
value: 83.56020942408377 |
|
- type: manhattan_recall |
|
value: 79.80000000000001 |
|
- type: max_accuracy |
|
value: 99.66534653465347 |
|
- type: max_ap |
|
value: 90.67883265196161 |
|
- type: max_f1 |
|
value: 82.81327389796928 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 54.25773656414605 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 32.52034918177213 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 47.10460797458404 |
|
- type: mrr |
|
value: 47.67126358119005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.159 |
|
- type: map_at_10 |
|
value: 0.9979999999999999 |
|
- type: map_at_100 |
|
value: 5.806 |
|
- type: map_at_1000 |
|
value: 16.575 |
|
- type: map_at_3 |
|
value: 0.391 |
|
- type: map_at_5 |
|
value: 0.596 |
|
- type: mrr_at_1 |
|
value: 56.00000000000001 |
|
- type: mrr_at_10 |
|
value: 68.7 |
|
- type: mrr_at_100 |
|
value: 68.892 |
|
- type: mrr_at_1000 |
|
value: 68.892 |
|
- type: mrr_at_3 |
|
value: 65.667 |
|
- type: mrr_at_5 |
|
value: 68.367 |
|
- type: ndcg_at_1 |
|
value: 51.0 |
|
- type: ndcg_at_10 |
|
value: 45.1 |
|
- type: ndcg_at_100 |
|
value: 36.834 |
|
- type: ndcg_at_1000 |
|
value: 39.329 |
|
- type: ndcg_at_3 |
|
value: 49.458 |
|
- type: ndcg_at_5 |
|
value: 48.177 |
|
- type: precision_at_1 |
|
value: 56.00000000000001 |
|
- type: precision_at_10 |
|
value: 47.8 |
|
- type: precision_at_100 |
|
value: 38.6 |
|
- type: precision_at_1000 |
|
value: 18.285999999999998 |
|
- type: precision_at_3 |
|
value: 54.0 |
|
- type: precision_at_5 |
|
value: 52.400000000000006 |
|
- type: recall_at_1 |
|
value: 0.159 |
|
- type: recall_at_10 |
|
value: 1.2510000000000001 |
|
- type: recall_at_100 |
|
value: 9.237 |
|
- type: recall_at_1000 |
|
value: 38.984 |
|
- type: recall_at_3 |
|
value: 0.44 |
|
- type: recall_at_5 |
|
value: 0.7080000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.6660000000000001 |
|
- type: map_at_10 |
|
value: 7.444000000000001 |
|
- type: map_at_100 |
|
value: 12.078 |
|
- type: map_at_1000 |
|
value: 13.716999999999999 |
|
- type: map_at_3 |
|
value: 4.06 |
|
- type: map_at_5 |
|
value: 5.172000000000001 |
|
- type: mrr_at_1 |
|
value: 20.408 |
|
- type: mrr_at_10 |
|
value: 33.547 |
|
- type: mrr_at_100 |
|
value: 35.281 |
|
- type: mrr_at_1000 |
|
value: 35.289 |
|
- type: mrr_at_3 |
|
value: 29.252 |
|
- type: mrr_at_5 |
|
value: 31.19 |
|
- type: ndcg_at_1 |
|
value: 18.367 |
|
- type: ndcg_at_10 |
|
value: 18.848000000000003 |
|
- type: ndcg_at_100 |
|
value: 29.938 |
|
- type: ndcg_at_1000 |
|
value: 42.792 |
|
- type: ndcg_at_3 |
|
value: 20.005 |
|
- type: ndcg_at_5 |
|
value: 18.617 |
|
- type: precision_at_1 |
|
value: 20.408 |
|
- type: precision_at_10 |
|
value: 17.143 |
|
- type: precision_at_100 |
|
value: 6.571000000000001 |
|
- type: precision_at_1000 |
|
value: 1.492 |
|
- type: precision_at_3 |
|
value: 21.088 |
|
- type: precision_at_5 |
|
value: 18.776 |
|
- type: recall_at_1 |
|
value: 1.6660000000000001 |
|
- type: recall_at_10 |
|
value: 12.736 |
|
- type: recall_at_100 |
|
value: 41.485 |
|
- type: recall_at_1000 |
|
value: 80.301 |
|
- type: recall_at_3 |
|
value: 5.137 |
|
- type: recall_at_5 |
|
value: 7.317 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 67.481 |
|
- type: ap |
|
value: 12.474830532963725 |
|
- type: f1 |
|
value: 51.720124230716834 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 55.62252405206565 |
|
- type: f1 |
|
value: 55.87133173318741 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 45.695133575997474 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.16284198605233 |
|
- type: cos_sim_ap |
|
value: 67.77133994574282 |
|
- type: cos_sim_f1 |
|
value: 63.007767732076914 |
|
- type: cos_sim_precision |
|
value: 60.89096726556732 |
|
- type: cos_sim_recall |
|
value: 65.27704485488127 |
|
- type: dot_accuracy |
|
value: 80.60439887941826 |
|
- type: dot_ap |
|
value: 55.17278808505333 |
|
- type: dot_f1 |
|
value: 55.023250784038055 |
|
- type: dot_precision |
|
value: 46.619021440351844 |
|
- type: dot_recall |
|
value: 67.12401055408971 |
|
- type: euclidean_accuracy |
|
value: 84.75889610776659 |
|
- type: euclidean_ap |
|
value: 69.33925609880741 |
|
- type: euclidean_f1 |
|
value: 64.72887151929653 |
|
- type: euclidean_precision |
|
value: 60.254661209640744 |
|
- type: euclidean_recall |
|
value: 69.92084432717678 |
|
- type: manhattan_accuracy |
|
value: 84.84234368480658 |
|
- type: manhattan_ap |
|
value: 69.50780726475959 |
|
- type: manhattan_f1 |
|
value: 64.78766430738119 |
|
- type: manhattan_precision |
|
value: 62.17855409995148 |
|
- type: manhattan_recall |
|
value: 67.62532981530343 |
|
- type: max_accuracy |
|
value: 84.84234368480658 |
|
- type: max_ap |
|
value: 69.50780726475959 |
|
- type: max_f1 |
|
value: 64.78766430738119 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.46198626149726 |
|
- type: cos_sim_ap |
|
value: 84.64911720373662 |
|
- type: cos_sim_f1 |
|
value: 77.18601251827143 |
|
- type: cos_sim_precision |
|
value: 75.19900679179142 |
|
- type: cos_sim_recall |
|
value: 79.28087465352634 |
|
- type: dot_accuracy |
|
value: 86.79512554818179 |
|
- type: dot_ap |
|
value: 80.43213280609042 |
|
- type: dot_f1 |
|
value: 74.18943791589976 |
|
- type: dot_precision |
|
value: 68.65828092243187 |
|
- type: dot_recall |
|
value: 80.68986757006468 |
|
- type: euclidean_accuracy |
|
value: 88.2368921488726 |
|
- type: euclidean_ap |
|
value: 84.2791000321804 |
|
- type: euclidean_f1 |
|
value: 76.62216238453198 |
|
- type: euclidean_precision |
|
value: 74.49640026179914 |
|
- type: euclidean_recall |
|
value: 78.87280566676932 |
|
- type: manhattan_accuracy |
|
value: 88.29122521054062 |
|
- type: manhattan_ap |
|
value: 84.25495067571485 |
|
- type: manhattan_f1 |
|
value: 76.60077590984667 |
|
- type: manhattan_precision |
|
value: 73.63784897350287 |
|
- type: manhattan_recall |
|
value: 79.81213427779488 |
|
- type: max_accuracy |
|
value: 88.46198626149726 |
|
- type: max_ap |
|
value: 84.64911720373662 |
|
- type: max_f1 |
|
value: 77.18601251827143 |
|
--- |
|
|
|
# embedder-100p |
|
|
|
This is a ms-marco bi-encoder from sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is trained on more than 20GiB of german text. It used the knowledge distillation to be a bi-language embedding model (English and German). |
|
|
|
<!--- Describe your model here --> |
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can use the model like this: |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["This is an example sentence", "Each sentence is converted"] |
|
|
|
model = SentenceTransformer('embedder-100p') |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
|
|
|
|
## Usage (HuggingFace Transformers) |
|
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModel |
|
import torch |
|
|
|
|
|
#Mean Pooling - Take attention mask into account for correct averaging |
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
|
|
|
|
|
# Sentences we want sentence embeddings for |
|
sentences = ['This is an example sentence', 'Each sentence is converted'] |
|
|
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('embedder-100p') |
|
model = AutoModel.from_pretrained('embedder-100p') |
|
|
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
|
# Compute token embeddings |
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
|
|
# Perform pooling. In this case, mean pooling. |
|
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
|
|
|
print("Sentence embeddings:") |
|
print(sentence_embeddings) |
|
``` |
|
|
|
|
|
|
|
## Evaluation Results |
|
|
|
<!--- Describe how your model was evaluated --> |
|
|
|
The evaluation on MTEB |
|
|
|
|
|
## Training |
|
The model was trained with the parameters: |
|
|
|
**DataLoader**: |
|
|
|
`torch.utils.data.dataloader.DataLoader` of length 231230 with parameters: |
|
``` |
|
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
|
``` |
|
|
|
**Loss**: |
|
|
|
`sentence_transformers.losses.MSELoss.MSELoss` |
|
|
|
Parameters of the fit()-Method: |
|
``` |
|
{ |
|
"epochs": 20, |
|
"evaluation_steps": 1000, |
|
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", |
|
"max_grad_norm": 1, |
|
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>", |
|
"optimizer_params": { |
|
"eps": 1e-06, |
|
"lr": 7e-06 |
|
}, |
|
"scheduler": "WarmupLinear", |
|
"steps_per_epoch": null, |
|
"warmup_steps": 5000, |
|
"weight_decay": 0.01 |
|
} |
|
``` |
|
|
|
|
|
## Full Model Architecture |
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
|
) |
|
``` |
|
|
|
## By |
|
@[bayang](https://huggingface.co/bayang) |
|
<!--- Describe where people can find more information --> |
